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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.tensor;
import org.junit.Test;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.fail;
public class TensorParserTestCase {
@Test
public void testEmpty() {
assertEquals(Tensor.Builder.of(TensorType.empty).cell(1).build(), Tensor.from("tensor():{{}:1}"));
assertEquals(Tensor.Builder.of(TensorType.empty).cell(10).build(), Tensor.from("10.0"));
assertEquals(Tensor.Builder.of(TensorType.empty).cell(10).build(), Tensor.from(TensorType.empty, "10.0"));
// looks like a hex string, but should not be interpreted as such:
assertEquals(Tensor.Builder.of(TensorType.empty).cell(10).build(), Tensor.from("0000000000000010"));
assertEquals(Tensor.Builder.of(TensorType.empty).cell(10).build(), Tensor.from(TensorType.empty, "0000000000000010"));
}
@Test
public void testSparseParsing() {
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor()")).build(),
Tensor.from("{}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(x{})")).cell(1.0, 0).build(),
Tensor.from("{{x:0}:1.0}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(x{})")).cell().label("x", "l0").value(1.0).build(),
Tensor.from("{{x:l0}:1.0}"));
assertEquals("If the type is specified, a dense tensor can be created from the sparse text form",
Tensor.Builder.of(TensorType.fromSpec("tensor(x[1])")).cell(1.0, 0).build(),
Tensor.from("tensor(x[1]):{{x:0 }:1.0}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(x{})")).cell().label("x", "..\",]}:..").value(1.0).build(),
Tensor.from("{{x:'..\",]}:..'}:1.0}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(x{})")).cell().label("x", "..'..").value(1.0).build(),
Tensor.from("{{x:\"..'..\"}:1.0}"));
}
@Test
public void testSingle() {
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[1])")).cell(1.0, 0).build(),
"tensor(x[1]):[1.0]");
}
@Test
public void testDenseParsing() {
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor()")).build(),
"tensor():{0.0}");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor()")).cell(1.3).build(),
"tensor():{1.3}");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[])")).cell(1.0, 0).build(),
"tensor(x[]):{0:1.0}");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[1])")).cell(1.0, 0).build(),
"tensor(x[1]):[1.0]");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[2])")).cell(1.0, 0).cell(2.0, 1).build(),
"tensor(x[2]):[1.0, 2.0]");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[2],y[3])"))
.cell(1.0, 0, 0)
.cell(2.0, 0, 1)
.cell(3.0, 0, 2)
.cell(4.0, 1, 0)
.cell(5.0, 1, 1)
.cell(6.0, 1, 2).build(),
"tensor(x[2],y[3]):[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[1],y[2],z[3])"))
.cell(1.0, 0, 0, 0)
.cell(2.0, 0, 0, 1)
.cell(3.0, 0, 0, 2)
.cell(4.0, 0, 1, 0)
.cell(5.0, 0, 1, 1)
.cell(6.0, 0, 1, 2).build(),
"tensor(x[1],y[2],z[3]):[[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]]");
assertDense(Tensor.Builder.of(TensorType.fromSpec("tensor(x[3],y[2],z[1])"))
.cell(1.0, 0, 0, 0)
.cell(2.0, 0, 1, 0)
.cell(3.0, 1, 0, 0)
.cell(4.0, 1, 1, 0)
.cell(5.0, 2, 0, 0)
.cell(-6.0, 2, 1, 0).build(),
"tensor(x[3],y[2],z[1]):[[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [-6.0]]]");
assertEquals("Skipping structure",
Tensor.Builder.of(TensorType.fromSpec("tensor(x[3],y[2],z[1])"))
.cell( 1.0, 0, 0, 0)
.cell( 2.0, 0, 1, 0)
.cell( 3.0, 1, 0, 0)
.cell( 4.0, 1, 1, 0)
.cell( 5.0, 2, 0, 0)
.cell(-6.0, 2, 1, 0).build(),
Tensor.from("tensor( x[3],y[2],z[1]) : [1.0, 2.0, 3.0 , 4.0, 5, -6.0]"));
var int8TT = TensorType.fromSpec("tensor<int8>(x[2],y[3])");
assertEquals("binary tensor A",
Tensor.Builder.of(int8TT)
.cell(1, 0, 0)
.cell(20, 0, 1)
.cell(127, 0, 2)
.cell(-1, 1, 0)
.cell(50, 1, 1)
.cell(-128, 1, 2).build(),
Tensor.from(int8TT, "01147FFF3280"));
assertEquals("binary tensor B",
Tensor.Builder.of(int8TT)
.cell(26.0, 0, 0)
.cell(0.0, 0, 1)
.cell(31.0, 0, 2)
.cell(-68.0, 1, 0)
.cell(-98.0, 1, 1)
.cell(-34.0, 1, 2).build(),
Tensor.from(int8TT, "1a001fbc9ede"));
assertEquals("binary tensor C",
Tensor.Builder.of(int8TT)
.cell(16, 0, 0)
.cell(32, 0, 1)
.cell(48, 0, 2)
.cell(-16, 1, 0)
.cell(-32, 1, 1)
.cell(-64, 1, 2).build(),
Tensor.from(int8TT, "102030F0E0C0"));
var floatTT = TensorType.fromSpec("tensor<float>(x[3])");
assertEquals("float tensor hexdump",
Tensor.Builder.of(floatTT)
.cell(0, 0)
.cell(1.25, 1)
.cell(-19.125, 2).build(),
Tensor.from(floatTT, "000000003FA00000c1990000"));
}
@Test
public void testDenseWrongOrder() {
assertEquals("Opposite order of dimensions",
Tensor.Builder.of(TensorType.fromSpec("tensor(x[3],y[2])"))
.cell(1, 0, 0)
.cell(4, 0, 1)
.cell(2, 1, 0)
.cell(5, 1, 1)
.cell(3, 2, 0)
.cell(6, 2, 1).build(),
Tensor.from("tensor(y[2],x[3]):[[1,2,3],[4,5,6]]"));
}
@Test
public void testMixedParsing() {
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(key{}, x[2])"))
.cell(TensorAddress.ofLabels("a", "0"), 1)
.cell(TensorAddress.ofLabels("a", "1"), 2)
.cell(TensorAddress.ofLabels("b", "0"), 3)
.cell(TensorAddress.ofLabels("b", "1"), 4).build(),
Tensor.from("tensor(key{}, x[2]):{a:[1, 2], b:[3, 4]}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(key{}, x[2])"))
.cell(TensorAddress.ofLabels(",:", "0"), 1)
.cell(TensorAddress.ofLabels(",:", "1"), 2)
.cell(TensorAddress.ofLabels("b", "0"), 3)
.cell(TensorAddress.ofLabels("b", "1"), 4).build(),
Tensor.from("tensor(key{}, x[2]):{',:':[1, 2], b:[3, 4]}"));
}
@Test
public void testSparseShortFormParsing() {
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(key{})"))
.cell(TensorAddress.ofLabels("a"), 1)
.cell(TensorAddress.ofLabels("b"), 2).build(),
Tensor.from("tensor(key{}):{a:1, b:2}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(key{})"))
.cell(TensorAddress.ofLabels("..\",}]:.."), 1)
.cell(TensorAddress.ofLabels("b"), 2).build(),
Tensor.from("tensor(key{}):{'..\",}]:..':1, b:2}"));
assertEquals(Tensor.Builder.of(TensorType.fromSpec("tensor(key{})"))
.cell(TensorAddress.ofLabels("..'.."), 1)
.cell(TensorAddress.ofLabels("b"), 2).build(),
Tensor.from("tensor(key{}):{\"..'..\":1, b:2}"));
}
@Test
public void testMixedWrongOrder() {
assertEquals("Opposite order of dimensions",
Tensor.Builder.of(TensorType.fromSpec("tensor(key{},x[3],y[2])"))
.cell(TensorAddress.ofLabels("key1", "0", "0"), 1)
.cell(TensorAddress.ofLabels("key1", "0", "1"), 4)
.cell(TensorAddress.ofLabels("key1", "1", "0"), 2)
.cell(TensorAddress.ofLabels("key1", "1", "1"), 5)
.cell(TensorAddress.ofLabels("key1", "2", "0"), 3)
.cell(TensorAddress.ofLabels("key1", "2", "1"), 6)
.cell(TensorAddress.ofLabels("key2", "0", "0"), 7)
.cell(TensorAddress.ofLabels("key2", "0", "1"), 10)
.cell(TensorAddress.ofLabels("key2", "1", "0"), 8)
.cell(TensorAddress.ofLabels("key2", "1", "1"), 11)
.cell(TensorAddress.ofLabels("key2", "2", "0"), 9)
.cell(TensorAddress.ofLabels("key2", "2", "1"), 12).build(),
Tensor.from("tensor(key{},y[2],x[3]):{key1:[[1,2,3],[4,5,6]], key2:[[7,8,9],[10,11,12]]}"));
assertEquals("Opposite order of dimensions",
Tensor.from("tensor(key{},x[3],y[2]):{key1:[[1,4],[2,5],[3,6]], key2:[[7,10],[8,11],[9,12]]}"),
Tensor.from("tensor(key{},y[2],x[3]):{key1:[[1,2,3],[4,5,6]], key2:[[7,8,9],[10,11,12]]}"));
}
@Test
public void testUnboundShortFormParsing() {
assertEquals(Tensor.from("tensor(x[]):[1.0, 2.0]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[])")).cell(1.0, 0).cell(2.0, 1).build());
assertEquals(Tensor.from("tensor<float>(x[]):[1.0, 2.0]"),
Tensor.Builder.of(TensorType.fromSpec("tensor<float>(x[])")).cell(1.0, 0).cell(2.0, 1).build());
assertEquals(Tensor.from("tensor<int8>(x[]):[1.0, 2.0]"),
Tensor.Builder.of(TensorType.fromSpec("tensor<int8>(x[])")).cell(1.0, 0).cell(2.0, 1).build());
assertEquals(Tensor.from("tensor<bfloat16>(x[]):[1.0, 2.0]"),
Tensor.Builder.of(TensorType.fromSpec("tensor<bfloat16>(x[])")).cell(1.0, 0).cell(2.0, 1).build());
assertEquals(Tensor.from("tensor(x[],y[]):[[1,2,3,4]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[])"))
.cell(1.0, 0, 0).cell(2.0, 0, 1).cell(3.0, 0, 2).cell(4.0, 0, 3).build());
assertEquals(Tensor.from("tensor(x[],y[]):[[1,2],[3,4]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[])"))
.cell(1.0, 0, 0).cell(2.0, 0, 1).cell(3.0, 1, 0).cell(4.0, 1, 1).build());
assertEquals(Tensor.from("tensor(x[],y[]):[[1],[2],[3],[4]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[])"))
.cell(1.0, 0, 0).cell(2.0, 1, 0).cell(3.0, 2, 0).cell(4.0, 3, 0).build());
assertEquals(Tensor.from("tensor(x[],y[],z[]):[[[1,2],[3,4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 0, 0, 1).cell(3.0, 0, 1, 0).cell(4.0, 0, 1, 1).build());
assertEquals(Tensor.from("tensor(x[],y[],z[]):[[[1],[2],[3],[4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 0, 1, 0).cell(3.0, 0, 2, 0).cell(4.0, 0, 3, 0).build());
assertEquals(Tensor.from("tensor(x[],y[],z[]):[[[1,2,3,4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 0, 0, 1).cell(3.0, 0, 0, 2).cell(4.0, 0, 0, 3).build());
assertEquals(Tensor.from("tensor(x[],y[],z[]):[[[1]],[[2]],[[3]],[[4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 1, 0, 0).cell(3.0, 2, 0, 0).cell(4.0, 3, 0, 0).build());
assertEquals(Tensor.from("tensor(x[],y[],z[]):[[[1, 2]],[[3, 4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 0, 0, 1).cell(3.0, 1, 0, 0).cell(4.0, 1, 0, 1).build());
assertEquals(Tensor.from("tensor(x[],y[],z[4]):[[[1,2,3,4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 0, 0, 1).cell(3.0, 0, 0, 2).cell(4.0, 0, 0, 3).build());
assertEquals(Tensor.from("tensor(x[2],y[],z[2]):[[[1, 2]],[[3, 4]]]"),
Tensor.Builder.of(TensorType.fromSpec("tensor(x[],y[],z[])"))
.cell(1.0, 0, 0, 0).cell(2.0, 0, 0, 1).cell(3.0, 1, 0, 0).cell(4.0, 1, 0, 1).build());
assertIllegal("Unexpected size 2 for dimension y for type tensor(x[],y[3])",
"tensor(x[],y[3]):[[1,2],[3,4]]");
}
private void assertDense(Tensor expectedTensor, String denseFormat) {
assertEquals(denseFormat, expectedTensor, Tensor.from(denseFormat));
assertEquals(denseFormat, expectedTensor.toString());
}
@Test
public void testIllegalStrings() {
assertIllegal("A dimension name must be an identifier or integer, not ''x''",
"{{'x':\"l0\"}:1.0}");
assertIllegal("A dimension name must be an identifier or integer, not '\"x\"'",
"{{\"x\":\"l0\", \"y\":\"l0\"}:1.0, {\"x\":\"l0\", \"y\":\"l1\"}:2.0}");
assertIllegal("At {x:0}: '1-.0' is not a valid double",
"{{x:0}:1-.0}");
assertIllegal("At value position 1: '1-.0' is not a valid double",
"tensor(x[1]):[1-.0]");
assertIllegal("At value position 5: Expected a ',' but got ']'",
"tensor(x[3]):[1, 2]");
assertIllegal("At value position 8: Expected a ']' but got ','",
"tensor(x[3]):[1, 2, 3, 4]");
}
private void assertIllegal(String message, String tensor) {
try {
Tensor.from(tensor);
fail("Expected an IllegalArgumentException when parsing " + tensor);
}
catch (IllegalArgumentException e) {
assertEquals(message, e.getCause().getMessage());
}
}
}
|
